These are the specific frameworks and obligations relevant to your sector, not a generic GDPR checklist. Each one has a direct implication for how you govern AI use and data handling.
Personal data in non-production environments violates the minimisation principle unless appropriate technical measures are in place.
Environment separation controls typically prohibit production data in dev and test without explicit data governance sign-off.
These are the specific workflows most organisations in your sector deploy first, in plain terms.
Take a representative subset of production. Preserve all foreign key relationships across the extracted tables. Anonymise PII in place. The result is a realistic dataset engineers can actually work with.
Configure once. VestraData refreshes your staging database automatically. Engineers always have a current, anonymised dataset. No ticket to raise, no DBA involvement.
Distribution, correlation, and null rates matched to production. Edge cases that exist in real data survive the anonymisation process. Load tests hit realistic cardinality.
Every masking script is technical debt. Schema changes break them. VestraData replaces the entire manual process: schema changes are detected automatically and masking rules update without human intervention.
Both products share the same detection engine. Most organisations in your sector start with one before adding the other.
Subsetting, anonymisation, and synthetic data generation for dev and test environments. Replaces manual masking scripts with an automated, scheduled pipeline. GDPR compliant by design.
Developers use AI for code generation, debugging, and IDE completions against staging data. VestraShield intercepts those sessions and ensures staging data content doesn't flow to external AI models.
Extract a representative subset while maintaining all foreign key relationships. Referential integrity across tables preserved.
When the production schema changes, masking rules update automatically. No manual script updates. No broken staging refreshes after migrations.
Configure a daily or weekly refresh. Staging database updated automatically. No DBA involvement, no ticket queue, no waiting.
Distribution, correlation, null rates, and cardinality matched to production. Load tests and edge-case tests behave as if running against production data.
Anonymised subsets imported directly into staging. Supports PostgreSQL, MySQL, SQL Server, and Oracle. No intermediate file step.
Only fields necessary for testing included in the subset. PII removed before data leaves the production environment.
GitHub Copilot, Cursor, and code assistant completions governed when running against staging data. Developers keep their tools; the data stays protected.
AI-assisted debugging sessions intercepted. Staging database content being queried or explained through AI tools doesn't reach external models.
Staging data patterns surfaced through AI code generation are intercepted before the prompt leaves your environment.
Different rules for permanent staff, contractors, and automated CI pipelines. Per-group configuration without separate deployments.
Every AI-assisted development session logged with entity inventory. Attributable to developer and tool. Hash-chained and tamper-evident.
Intercept runs inside your environment. Staging data never reaches external AI infrastructure unprotected.
We connect to something real in your environment and you see actual findings. No slide decks. No fabricated data. Median time to first scan: under 4 hours from credentials.
For engineering leads and DevOps teams. We can walk through your staging environment setup.